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Large-scale analysis reveals a novel risk score to predict overall survival in hepatocellular carcinoma

Authors Zheng YJ, Liu YL, Zhao SF, Zheng ZT, Shen CY, An L, Yuan YL

Received 25 July 2018

Accepted for publication 25 October 2018

Published 21 November 2018 Volume 2018:10 Pages 6079—6096

DOI https://doi.org/10.2147/CMAR.S181396

Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Ms Justinn Cochran

Peer reviewer comments 2

Editor who approved publication: Dr Ahmet Emre Eskazan


Yujia Zheng,1,* Yulin Liu,1,* Songfeng Zhao,2 Zhetian Zheng,3 Chunyi Shen,1 Li An,4 Yongliang Yuan2

1Biotherapy Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China; 2Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China; 3School of Computer Science, Yangtze University, Jingzhou, Hubei, China; 4Institute of Quality Standard and Testing Technology for Agro-products, Henan Academy of Agricultural Sciences, Zhengzhou, China

*These authors contributed equally to this work

Background: Hepatocellular carcinoma (HCC) is a major cause of cancer mortality and an increasing incidence worldwide; however, there are very few effective diagnostic approaches and prognostic biomarkers.
Materials and methods: One hundred forty-nine pairs of HCC samples from Gene Expression Omnibus (GEO) were obtained to screen differentially expressed genes (DEGs) between HCC and normal samples. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, Gene ontology enrichment analyses, and protein–protein interaction network were used. Cox proportional hazards regression analysis was used to identify significant prognostic DEGs, with which a gene expression signature prognostic prediction model was identified in The Cancer Genome Atlas (TCGA) project discovery cohort. The robustness of this panel was assessed in the GSE14520 cohort. We verified details of the gene expression level of the key molecules through TCGA, GEO, and qPCR and used immunohistochemistry for substantiation in HCC tissues. The methylation states of these genes were also explored.
Results: Ninety-eight genes, consisting of 13 upregulated and 85 downregulated genes, were screened out in three datasets. KEGG and Gene ontology analysis for the DEGs revealed important biological features of each subtype. Protein–protein interaction network analysis was constructed, consisting of 64 nodes and 115 edges. A subset of four genes (SPINK1, TXNRD1, LCAT, and PZP) that formed a prognostic gene expression signature was established from TCGA and validated in GSE14520. Next, the expression details of the four genes were validated with TCGA, GEO, and clinical samples. The expression panels of the four genes were closely related to methylation states.
Conclusion: This study identified a novel four-gene signature biomarker for predicting the prognosis of HCC. The biomarkers may also reveal molecular mechanisms underlying development of the disease and provide new insights into interventional strategies.

Keywords: hepatocellular carcinoma, GEO, TCGA, biomarker, differentially expressed genes

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